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Deuteration has emerged as a significant method for investigating kinetic isotope effects in chemical reactions. In recent years, research on deuteration has revealed that this reaction often alters the pharmacokinetic or toxicological properties of drug molecules, thereby enhancing their therapeutic effect. Therefore, deuteration is considered as a potential avenue for drug research direction. Since the approval of the first deuterated drug, "deutetrabenazine," by the American Food and Drug Administration (FDA) in 2017, deuteration has been applied in the development of a variety of novel drugs. Research on deuteration technology has matured with the publication of numerous deuteration strategies. This article reviews recently reported strategies for late‐stage selective deuteration, discusses the challenges associated with current deuteration strategies, and presents future prospects for the synthesis of deuterated drugs. [ABSTRACT FROM AUTHOR]

Clustering the trajectories of vehicles moving on road networks is a key data mining technique for understanding human mobility patterns, as well as their interactions with urban environments. The development of efficient and scalable trajectory clustering algorithms, however, still faces challenges because of the computational costs when measuring similarities among a large number of network-constrained trajectories. To address this problem, a novel trajectory clustering framework based on the well-developed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach is proposed. This proposed framework accurately quantifies similarities using a trajectory representation of continuous polylines in the space and time dimensions, and does not require trajectory discretization. Further, the proposed framework utilizes the space-time buffering concept to formulate ε -neighborhood queries that directly retrieve the ε -neighbors of trajectories and thus avoids computing a trajectory similarity matrix. State-of-the-art trajectory databases and index structures are incorporated to further improve trajectory clustering performance. A comprehensive case study was carried out using an open dataset of 20,161 trajectories. Results show that the proposed framework efficiently executed trajectory clustering on the large test dataset within 3 min. This was approximately 2,700 times faster than existing DBSCAN algorithms. [ABSTRACT FROM AUTHOR]

The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities. [ABSTRACT FROM AUTHOR]

By means of β-Zn4Sb3 addition and thermal decomposition, dual nanoinclusions of Zn and ZnSb were introduced and Zn atoms were doped into p-type Bi0.4Sb1.6Te3 successfully. Due to the increase of hole concentration by Zn doping and band structure optimization, the bipolar conduction was suppressed and the intrinsic excitation shifts to higher temperature. The power factors of the samples were slightly improved, while the lattice thermal conductivities of the samples were greatly reduced due to the extra phonon scattering introduced by the dual nanoinclusions. As a result, the critical temperature corresponding to the maximum ZT value was greatly increased to 423 K, which is about 120 K higher when compared with the conventional Bi2Te3-based materials. A maximum ZT of 1.44 was achieved for the sample with 1.5 wt% β-Zn4Sb3 at 423 K, which is also the highest ZT value ever reported at such a high temperature for p-type Bi2Te3-based materials. This work is of importance to expand the application of Bi2Te3-based materials for low grade waste-heat recovery. [ABSTRACT FROM AUTHOR]